class_docstr: Building Block of RepLKNet
  
  This class defines overparameterized large kernel conv block
  introduced in `RepLKNet <https://arxiv.org/abs/2203.06717>`_
  
  Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
docstr: Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
  https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
  architecture used at training time to obtain a plain CNN-like structure
  for inference.
docstr_tok: ["Following","works","like","`","RepVGG",":","Making","VGG-style","ConvNets","Great","Again","`","-","https",":","\/\/arxiv.org\/pdf\/2101.03697.pdf",".","We","re-parameterize","multi-branched","architecture","used","at","training","time","to","obtain","a","plain","CNN-like","structure","for","inference","."]
code: def reparameterize(self) -> None:
      """
      Following works like `RepVGG: Making VGG-style ConvNets Great Again` -
      https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched
      architecture used at training time to obtain a plain CNN-like structure
      for inference.
      """
      eq_k, eq_b = self.get_kernel_bias()
      self.lkb_reparam = nn.Conv2d(
          in_channels=self.in_channels,
          out_channels=self.out_channels,
          kernel_size=self.kernel_size,
          stride=self.stride,
          padding=self.padding,
          dilation=self.lkb_origin.conv.dilation,
          groups=self.groups,
          bias=True,
      )
  
      self.lkb_reparam.weight.data = eq_k
      self.lkb_reparam.bias.data = eq_b
      self.__delattr__("lkb_origin")
      if hasattr(self, "small_conv"):
          self.__delattr__("small_conv")
  
file_path: ml-fastvit/models/modules/replknet.py
imports: from typing import Tuple
  import torch
  import torch.nn
label_desc: Use image node_id 4 for calling the ReparamLargeKernelConv obj's underlying member method code with example usage: obj.reparameterize() without return types